Several color-imaging algorithms such as color gamut mapping to a target device and resizing of color images have traditionally involved pixel-wise operations. That is, each color value is processed independent of its neighbors in the image. In recent years, applications such as spatial gamut mapping have demonstrated the virtues of incorporating spatial context into color processing tasks. In this paper, we investigate the use of locally based measures of image complexity such as the entropy to enhance the performance of two color imaging algorithms viz. spatial gamut mapping and content-aware resizing of color images. When applied to spatial gamut mapping (SGM), the use of these spatially based local complexity measures helps adaptively determine gamut mapping parameters as a function of image content - hence eliminating certain artifacts commonly encountered in SGM algorithms. Likewise, developing measures of complexity of color-content in a pixel neighborhood can help significantly enhance performance of content-aware resizing algorithms for color images. While the paper successfully employs intuitively based measures of image complexity, it also aims to bring to light potentially greater rewards that may be reaped should more formal measures of local complexity of color content be developed.